Course curriculum

    1. Facilitator

    1. Introduction

    2. Unstructured and Semi-Structured Data

    3. Data Types

    4. Data Structures and Formats

    5. Data Tools

    6. Data Life Cycle

    7. Key Takeaways

    8. A Memo to the CEO

    9. Assignment 1 Submission

    10. Module 2 Glossary

    1. Introduction

    2. What are Analytics?

    3. Big Data

    4. Data Management

    5. Analysis

    6. Key Takeaways

    7. Making Meaning of Visual Data

    8. Assignment 2 Submission

    9. Module 3 Glossary

    1. Introduction

    2. What is Data Visualization?

    3. Before You Begin

    4. Gestalt Principles

    5. Industry Best Practices

    6. Accessibility in Data Visualization

    7. Choosing the Right Chart Type

    8. Data Visualization Tips

    9. Statistics Overview

    10. Key Takeaways and Test Yourself

    11. Module 4 Glossary

    1. Introduction

    2. How AI is Used in Data Analytics

    3. Key Concerns About AI

    4. Key Takeaways and Test Yourself

    5. Module 5 Glossary

About this course

  • Start Any Time
  • Learn at Your Own Pace
  • ~ 3 Hours per Module


  • Pre-Requisites

    A basic understanding of statistical concepts such as mean, median, mode, standard deviation, and hypothesis testing. Prior experience handling datasets and performing basic data manipulation tasks.

  • Technical Requirements

    A computer (Windows, Mac, or Linux). A stable internet connection to download datasets and access cloud-based visualization tools.

  • Software Requirements

    Microsoft Excel or a similar spreadsheet tool capable of performing data analysis functions and creating charts. Visualization software such as Tableau or similar (free or student versions are acceptable for the course). Familiarity with installing software and managing files and folders on a computer.